What is AI security testing?

AI security testing assesses the security of AI and LLM systems including prompt injection, data poisoning, output manipulation, RAG pipeline vulnerabilities, and supply chain risks. It goes beyond traditional application security to test the model itself.

Does my company need AI security testing?

If your organisation uses AI or LLM systems in any customer-facing or data-sensitive capacity, yes. 67% of organisations using AI have not tested it for security. A single prompt injection vulnerability can expose customer data or enable fraud.

How is AI security testing different from regular penetration testing?

Traditional penetration testing focuses on infrastructure and application layers. AI security testing specifically targets the model, training data, RAG pipelines, plugin integrations, and AI-specific attack vectors that standard testing does not cover.

What compliance requirements cover AI security?

The EU AI Act requires risk assessment of AI systems. NIS2 covers AI systems in critical infrastructure. OWASP has published a Top 10 for LLM applications. OFFCEPT tests against all of these frameworks.

OFFCEPT

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All Engagements

Real testing by experienced operators. Threat-informed scoping, actionable reporting, and verified remediation.

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What We Do

Penetration Testing

Manual testing that finds what scanners miss

Red Team Operations

Multi-week adversary simulations

CTEM

Continuous threat exposure management

Threat Intelligence

Data breach, dark web, and credential monitoring

EDR & XDR Testing

Endpoint detection gap analysis and evasion testing

Phishing & Social Engineering

Real campaigns, not generic templates

AI & LLM Security

Testing AI systems and LLM integrations

Zero-Day Research

Hunting undiscovered vulnerabilities

Reverse Engineering

Binary analysis, malware, and firmware

How We Work

From scoping to verified remediation in four phases. Every engagement follows the same structure, adapted to your threat landscape.

Full Breakdown

The Process

01 Scoping & Threat Model

Profile threat actors, map attack surface, define objectives

02 Testing & Exploitation

Manual testing, chained vulnerabilities, real attack paths

03 Reporting & Debrief

Proof, impact, remediation path, live walkthrough

04 Remediation & Validation

Re-test fixes, confirm they work, measurable improvement

Blog Open Source Tools Case Studies

AI & LLM Security

Prompt injection, data poisoning, model manipulation, output exploitation. Different problems requiring different expertise. Our team has been testing AI systems since the technology first appeared.

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Test the model, not just the API

Most AI security assessments stop at the API. We go deeper, throwing adversarial inputs at the model, testing the training pipeline, and checking whether safety guardrails actually hold up when someone is trying to break them.

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We throw everything at your LLM's input handling to find prompt injection paths that bypass guardrails, leak system prompts, or make the model do things it should not.

We check your training pipeline for data poisoning risks: where the data comes from, how it is validated, and how the model behaves when someone feeds it adversarial inputs.

We test for hallucinations, PII leaks in generated text, unsafe code suggestions, and output that could be used for social engineering or fraud.

RAG pipelines, external tool integrations, and plugins all expand what your model can do, and what an attacker can exploit. We assess all of it.

Third-party models, pre-trained weights, external APIs. We check your entire AI supply chain for vulnerabilities you inherited from someone else.

Attack Categories

OWASP

LLM Top 10

Model

Security Audit

RAG

Pipeline Review

How It Works

Purpose-built for AI attack surfaces

Model & Pipeline Review

We map out your entire AI setup: what model you are running, where the training data comes from, how it is deployed, what interfaces face users, and what third-party services are plugged in.

Adversarial Testing

Prompt injection, jailbreaks, data extraction, output manipulation. We throw all of it at your model to see if the safety guardrails and input validation actually work.

Data & Supply Chain

We check the training data, RAG pipeline security, plugin authentication, and every third-party dependency in your AI stack for supply chain vulnerabilities.

Report & Harden

Every finding comes with reproduction steps, risk rating, and a specific fix, whether that is input sanitisation, output filtering, or model-level guardrails.

What We Test For

Four attack categories. One assessment.

Critical

Prompt Injection

Inputs designed to bypass guardrails, leak system prompts, or make the model generate harmful content. We test direct injection, indirect injection, and multi-turn attack chains.

High

Data Poisoning

What happens if someone poisons your training data or fine-tuning set? We test for backdoors, output bias, and vulnerabilities that activate under specific trigger inputs.

High

Output Exploitation

Models can leak PII in their output, hallucinate convincing misinformation, generate unsafe code, or produce content that helps with social engineering. We test for all of it.

Medium

Supply Chain

Compromised model weights, malicious plugins, insecure RAG data sources, and API dependencies that bring vulnerabilities along with them.

Case Study

Fintech startup fixes critical prompt injection path before product launch

Critical Prompt InjectionPre-Launch AssessmentFintech

OFFCEPT found a prompt injection chain that let them pull other customers' financial data through our AI assistant. We were two weeks from launch. Four days later the vulnerability was fully closed.

Co-Founder & CTO

AI-Powered Fintech Startup

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Your AI has gaps you have not considered

Prompt injection, data poisoning, model manipulation. Standard security assessments do not cover any of it. Let us find the gaps in your AI stack before someone with different motivations does.

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